Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

How does pre-reduction MRI affect surgeon's behaviour when reducing Distraction-Flexion injuries of the cervical spine?

Includes abstract. Includes bibliographical references

Comparison of Eye Movement Data to Direct Measures of Situation Awareness for Development of a Novel Measurement Technique in Dynamic, Uncontrolled Test Environments

Situation awareness (SA) is a measure of an individual\u27s knowledge and understanding of the current and expected future states of a situation. While there are numerous options for SA measurement, none are currently suitable in dynamic, uncontrolled environments. Direct measures of SA are the most common, but require a large amount of researcher control as well as the ability to stop operators during a task in order to ask questions about their levels of SA. The current research explored the relationship between direct measures of SA and eye tracking measures as a first step in the development of an unobtrusive SA measure to be used in less controllable, dynamic environments. Two studies compared participant eye movements and SA in driving and air traffic control scenarios. Both studies showed that the more individuals fixated on an important, task-relevant event, the higher their SA for that event. The studies also provide evidence that the way operators allocate attention (i.e., distributed widely or narrowly) affects their SA as well as their task performance. In addition, study 2 results showed positive correlations between SA and task performance. The results indicate that eye tracking may be a viable option for measuring SA in environments not conducive to current direct SA measurement techniques. Future research should continue to explore which eye movement variables best predict participant SA, as well as to investigate the relationship between attention allocation and SA

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 192

This bibliography lists 247 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1979

Supporting Situation Awareness and Decision Making in Weather Forecasting

Weather forecasting is full of uncertainty, and as in domains such as air traffic control or medical decision making, decision support systems can affect a forecaster’s ability to make accurate and timely judgments. Well-designed decision aids can help forecasters build situation awareness (SA), a construct regarded as a component of decision making. SA involves the ability to perceive elements within a system, comprehend their significance, and project their meaning into the future in order to make a decision. However, how SA is affected by uncertainty within a system has received little attention. This tension between managing uncertainty, situation assessment, and the impact that technology has on the two, is the focus of this dissertation. To address this tension, this dissertation is centered on the evaluation of a set of coupled models that integrate rainfall observations and hydrologic simulations, coined “the FLASH system” (Flooded Locations and Simulated Hydrographs project). Prediction of flash flooding is unique from forecasting other weather-related threats due to its multi-disciplinary nature. In the United States, some weather forecasters have limited hydrologic forecasting experience. Unlike FLASH, current flash flood forecasting tools are based upon rainfall rates, and with the recent expansion into coupled rainfall and hydrologic models, forecasters have to learn quickly how to incorporate these new data sources into their work. New models may help forecasters to increase their prediction skill, but no matter how far the technology advances, forecasters must be able to accept and integrate the new tools into their work in order to gain any benefit. A focus on human factors principles in the design stage can help to ensure that by the time the product is transitioned into operational use, the decision support system addresses users’ needs while minimizing task time, workload, and attention constraints. This dissertation discusses three qualitative and quantitative studies designed to explore the relationship between flash flood forecasting, decision aid design, and SA. The first study assessed the effects of visual data aggregation methods on perception and comprehension of a flash flood threat. Next, a mixed methods approach described how forecasters acquire SA and mitigate situational uncertainty during real-time forecasting operations. Lastly, the third study used eye tracking assessment to identify the effects of an automated forecasting decision support tool on SA and information scanning behavior. Findings revealed that uncertainty management in forecasting involves individual, team, and organizational processes. We make several recommendations for future decision support systems to promote SA and performance in the weather forecasting domain

Relational Model Bases: A Technical Approach to Real-time Business Intelligence and Decision Making

This article presents a technical approach to acquiring quality, real-time decision-making information within organizations and illustrates this approach with an extended case study. Using relational model bases for real-time, operational decision making in organizations facilitates a transition to dynamic (vs. forecast-driven) resource allocation decisions. These and related systems offer development of a new generation of DSS applications which can be applied to extend preemptive decision making across many industries. This approach is illustrated through a description of a detailed conceptual case (scenario) pertaining to its application in agribusiness. This approach to decision making can be viewed as an extension of well-known techniques pertaining to DSS but also represents the opportunity to address problems not amenable to traditional post hoc analysis. Researchers can learn from the accumulated knowledge pertaining to DSS but can also examine innovations that push forward into new territories. The article presents and discusses a variety of emergent research questions prompted by the application of these technologies in the business environment

Variability and similarity of inter-beat intervals of the heart as markers of perceived stress and behavioral regulation

The current dissertation investigated inter-beat interval (IBI) indices of variability and similarity, reflecting autonomic nervous system (ANS) modulation on heart rate. IBI indices of cardiac vagal activity (CVA) are further considered to reflect activity in brain areas involved in self regulation. Yet, it is unclear which specific aspect(s) of self-regulation such IBI indices load most highly on, and their relation to contextual factors. Thus, in a sample of college students (n = 143) in paper I, we investigated how CVA and perceived stress associated with contextual factors of perceived social support and sex. Moreover, we expected indices to load highly on the internal regulation of perceived stress, compared to the external regulation of behavior. This was examined in adolescents with attention-deficit/hyperactivity disorder (ADHD) and controls (n = 67) in paper II. In paper III, we investigated the use of a nonlinear, graph theory-based method for illustrating IBI differences in adolescents with ADHD and controls (n = 73). In all studies, IBI indices were derived from short-term resting electrocardiogram (ECG) recordings, with high frequency-heart rate variability (HF-HRV) as the applied measure of CVA. Self-report questionnaires assessed emotion regulation difficulties (the Difficulties in Emotion Regulation Scale) perceived stress (the Perceived Stress Scale), and perceived social support (The Medical Outcomes Study Social Support Survey). In the moderation analysis of paper I, CVA associated positively with perceived social support in females with intermediate and high, compared to low, perceived stress levels, but not in males. Linear regression analyses in paper II showed that CVA associated negatively with access to emotion regulation strategies in adolescents with ADHD and controls. In paper III, independent samples t-test showed that the similarity graph algorithm illustrated IBI differences between the ADHD and control groups which traditional CVA analyses did not. In sum, the studies suggest that CVA might mark perceived stress regulation, and emphasize the consideration of contextual factors such as perceived social support and sex in the interpretation of this marker. Furthermore, the similarity graph algorithm might increase the sensitivity of IBI markers, possibly also indexing behavioral regulation. Although further research is required, IBI markers might have potential clinical use in the diagnosis, monitoring and treatment of psychiatric disorders.Doktorgradsavhandlin

GRASP News Volume 9, Number 1

A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory